Equalization techniques using viterbi algorithms in software-defined radio systems

ABSTRACT

A system and method for channel equalization using a Viterbi algorithm. Information from an output of a matched filter and channel parameters from a channel estimation circuit are correlated and passed on to a reconfigurable data path. The reconfigurable data path includes a reconfigurable branch metric calculation block. The reconfigurable data path also includes a reconfigurable add-compare-select and path metric calculation block. The reconfigurable data path is controlled using a programmable finite state machine. The programmable finite state machine executes a plurality of context-related instructions associated with the Viterbi algorithm. The system and method for channel equalization supports multiple standards using Viterbi algorithms.

CROSS-REFERENCE TO RELATED APPLICATION AND CLAIM OF PRIORITY

This application is a continuation-in-part of U.S. patent application Ser. No. 11/314,460, filed Dec. 21, 2005, entitled “Viterbi Decoder Architecture for use in Software-defined Radio Systems”. U.S. patent application Ser. No. 11/314,460 is assigned to the assignee of the present application and is incorporated by reference into this disclosure as if fully set forth herein. This disclosure hereby claims priority under 35 U.S.C. §120 to U.S. patent application Ser. No. 11/314,460. U.S. patent application Ser. No. 11/314,460 issued as U.S. Pat. No. 7,603,613 on Oct. 13, 2009.

This application is related to U.S. Provisional Patent No. 60/839,271, filed Aug. 22, 2006, entitled “Method and Apparatus for Equalization Techniques using Viterbi Algorithm in Software Defined Radio”. U.S. Provisional Patent No. 60/839,271 is assigned to the assignee of this application and is incorporated by reference into this disclosure as if fully set forth herein. This application hereby claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent No. 60/839,271.

TECHNICAL FIELD OF THE INVENTION

The present application relates generally to communication systems and, more specifically, to equalization techniques using Viterbi algorithms.

BACKGROUND OF THE INVENTION

To improve throughput, many communication systems implement some type of forward error correction (FEC) technique. This is particularly true of wireless communication systems, such as cellular networks. One of the most common FEC techniques is known as convolutional coding with Viterbi decoding. Convolutional coding with Viterbi decoding is particularly useful for channels in which additive white Gaussian noise (AWGN) distorts the transmitted signal.

A Viterbi decoder is the maximum likelihood sequence decoder for a convolutional code. Viterbi decoders have been adopted for use in the physical layers of a number of wireless standards, including WCDMA, CDMA2000, IEEE-802.16e (i.e., WiBro) and others. These standards operate at different data rates and implement convolutional encoders in the transmitters and Viterbi decoders in the receivers that are optimized for only one or two specific standards. Also, most conventional convolutional encoders and Viterbi decoders operate at relatively low data rates.

A software-defined radio (SDR) device uses reconfigurable hardware that may be programmed over-the-air to operate under different wireless standards. For example, an SDR transceiver in a wireless laptop computer or PDA may be configured by different software loads to operate in an IEEE-802.11x wireless network, a CDMA2000 wireless network, an OFDM/OFDMA wireless network, a GSM wireless network, or another type of network. Many of these wireless standards require the use of Viterbi decoders.

However, conventional Viterbi decoders have significant drawbacks with respect to SDR applications. As noted above, conventional Viterbi decoders are optimized for decoding under only one or two specific (and similar) standards. If an SDR device must support many wireless standards, more than one Viterbi decoder must be implemented in the SDR device. This leads to complex transceiver designs that inefficiently use chip space and consume an excessive amount of power.

Also, many new wireless standards operate at relatively high data rates (e.g., WiBro, HSPDA, and the like). A Viterbi decoder that is optimized in terms of speed and power consumption for a low data rate standard is unlikely to be optimized in terms of speed and power consumption for a high data rate standard, and vice versa. Thus, conventional Viterbi decoders are not suitable for SDR applications.

Therefore, there is a need for improved channel equalization techniques. In particular, there is a need for Viterbi decoders with channel equalization algorithms.

SUMMARY OF THE INVENTION

The present disclosure provides channel equalization techniques using Viterbi algorithms.

In one embodiment, the present disclosure provides an apparatus for channel equalization using a Viterbi algorithm. The reconfigurable data path includes a reconfigurable branch metric calculation block correlating information from an output of a matched filter and channel parameters from a channel estimation circuit. The reconfigurable data path also includes a reconfigurable add-compare-select and path metric calculation block. The Viterbi algorithm also includes a programmable finite state machine capable of controlling the reconfigurable data path, wherein the programmable finite state machine to execute a plurality of context-related instructions associated with the Viterbi decoder.

In another embodiment, the present disclosure provides a software-defined radio (SDR) system. The SDR system includes a reconfigurable radio-frequency (RF) transceiver portion to operate under a plurality of wireless communication standards. The SDR system also includes a reconfigurable baseband processing portion associated with the RF transceiver portion. The reconfigurable baseband processing portion includes a Viterbi algorithm having a reconfigurable data path. The reconfigurable data path includes a reconfigurable branch metric calculation block correlating information from an output of a matched filter and channel parameters from a channel estimation circuit. The reconfigurable data path also includes a reconfigurable add-compare-select and path metric calculation block. The SDR system includes a programmable finite state machine to control the reconfigurable data path. The programmable finite state machine executes a plurality of context-related instructions associated with the reconfigurable baseband processing portion.

In still another embodiment, the present disclosure provides a method of channel equalization using a Viterbi algorithm. The method includes correlating information from an output of a matched filter and channel parameters from a channel estimation circuit and passing the correlated information to a reconfigurable data path. The reconfigurable data path includes a reconfigurable branch metric calculation block and a reconfigurable add-compare-select and path metric calculation block. The method also includes controlling the reconfigurable data path with a programmable finite state machine, wherein the programmable finite state machine executes a plurality of context-related instructions associated with the Viterbi algorithm.

Before undertaking the DETAILED DESCRIPTION OF THE INVENTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document: the terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation; the term “or,” is inclusive, meaning and/or; the phrases “associated with” and “associated therewith,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, or the like; and the term “controller” means any device, system or part thereof that controls at least one operation, such a device may be implemented in hardware, firmware or software, or some combination of at least two of the same. It should be noted that the functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. Definitions for certain words and phrases are provided throughout this patent document, those of ordinary skill in the art should understand that in many, if not most instances, such definitions apply to prior, as well as future uses of such defined words and phrases.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts:

FIG. 1 is a high-level block diagram of a CRISP device that may be used to implement a reconfigurable Viterbi decoder;

FIG. 2 is a high-level block diagram of a reconfigurable processing system;

FIG. 3 is a high-level block diagram of a multi-standard software-defined radio (SDR) system that implements a reconfigurable Viterbi decoder using at least one CRISP device;

FIG. 4 is a block diagram of a reconfigurable Viterbi decoder implemented in a CRISP according to the principles of the disclosure;

FIG. 5 is timing diagram of selected pipeline stages in a convolution encoder;

FIG. 6 is timing diagram of selected pipeline stages in a branch metric calculation block;

FIG. 7 is timing diagram of selected pipeline stages in a path metric calculation block;

FIG. 8 is timing diagram of selected pipeline stages in a trellis and traceback calculation block;

FIG. 9 illustrates an exemplary occurrence of ISI between a base station and a mobile station;

FIGS. 10A and 10B are somewhat simplified block diagram of a MLSE channel equalizer according to one embodiment of the present disclosure;

FIG. 11 is a somewhat simplified block diagram of a Viterbi CRISP according to one embodiment of the present disclosure; and

FIG. 12 is a somewhat simplified block diagram of a channel equalization method using Viterbi algorithms according to one embodiment of the present disclosure

DETAILED DESCRIPTION OF THE INVENTION

FIGS. 1 through 12, discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged processing system.

The present disclosure provides channel equalization techniques using Viterbi algorithms. The disclosed techniques provide a high degree of parallelism to support high data rate standards. For example, a target downlink rate of WiBro is 12 Mbps and HSDPA achieves a peak data rate up to 14.4 Mbps. In one embodiment, the present disclosure uses parallel execution units as well as pipelining techniques to achieve such high data rates.

The disclosed techniques supports multimode operation for decoding in different communication standards, including WCDMA, HSDPA, CDMA2000, IEEE-802.16e (i.e., WiBro), and GSM/EDGE, among others. The disclosed techniques also provide adaptability to support different data rates. WiBro and WCDMA/HSDPA operate at many different data rates. The disclosed techniques are optimized not only for the maximum data rates but also for different ranges of data rate.

A reconfigurable Viterbi decoder also minimizes power consumption for different standards and data rates, since the Viterbi decoder may be implemented in handheld devices. Finally, a reconfigurable Viterbi decoder according to one embodiment of the present disclosure provides flexibility to the end user. Although the design targets Viterbi decoding, the architecture does not require stringent parameter restrictions. Thus, the design provides a general Viterbi decoder platform that enables end users to select parameters and algorithms based on end-user requirements.

The reconfigurable Viterbi decoder described herein is implemented using a context-based operation reconfigurable instruction set processor (CRISP) device. CRISP devices are described in detail in U.S. patent application Ser. No. 11/123,313 filed on May 6, 2005 and entitled “Context-based Operation Reconfigurable Instruction Set Processor and Method of Operation”. U.S. patent application Ser. No. 11/123,313 is assigned to the assignee of this application and is hereby incorporated by reference into this application as if fully set forth herein.

Similarly, CRISP devices are also described in U.S. Provisional Patent No. 60/682,339, filed May 18, 2005, entitled “Viterbi Decoder Architecture For Software-Defined Radio” and U.S. Provisional Patent No. 60/653,968, filed Feb. 17, 2005, entitled “Context-Based Operation Reconfigurable Instruction Set Processor” also generally describe the CRISP architecture. U.S. Provisional Patent Nos. 60/682,339 and 60/653,968 are assigned to the assignee of this application and are hereby incorporated by reference into this disclosure as if fully set forth herein.

FIG. 1 is a high-level block diagram of context-based operation reconfigurable instruction set processor (CRISP) 100, which may be used to implement the disclosed techniques. CRISP 100 implements only a subset of context-related instructions in an optimum manner. CRISP 100 comprises memory 110, programmable data path circuitry 120, programmable finite state machine (FSM) 130, and optional program memory 140. A context is a group of instructions of a data processor that are related to a particular function or application, such as Viterbi decoding instructions.

CRISP 100 defines a generic hardware block that usually consists of higher level hardware processor blocks. The principle advantage to CRISP 100 is that CRISP 100 breaks down the required application into two main domains, a control domain and a data path domain, and optimizes each domain separately. By performing Viterbi decoding in CRISP 100, the disclosed Viterbi decoder reduces the problems related to flexibility and power consumption that affect conventional Viterbi decoders.

The control domain is implemented by programmable FSM 130, which may comprise a digital signal processor (DSP), a controller, or another conventional processing device. Programmable FSM 130 is configured by reconfiguration bits received from an external controller (not shown). Programmable FSM 130 may execute a program stored in associated optional program memory 140. The program may be stored in program memory 140 via the DATA line from an external controller (not shown). Memory 110 is used to store application data used by data path circuitry 120.

Programmable data path circuitry 120 is divided into sets of building blocks that perform particular functions (e.g., registers, multiplexers, multipliers, and the like). Each of the building blocks is both reconfigurable and programmable to allow maximum flexibility. The division of programmable data path circuitry 120 into functional blocks depends on the level of reconfigurability and programmability required for a particular application. In the context of a Viterbi decoder, these functional blocks may include a branch metric calculation block, an add-compare-select block, a path metric calculation block, a trellis and traceback block, buffers, random access memory (RAM), and other blocks, as described below in greater detail.

Since different contexts are implemented by separate CRISP devices that work independently of other CRISP devices, implementing a Viterbi decoder using one or more CRISP devices provides an efficient power management scheme that is able to shut down a CRISP device when the CRISP device is not required. This assures that only the CRISP devices that are needed at a given time are active, while other idle CRISP devices do not consume significant power.

FIG. 2 is a high-level block diagram of reconfigurable processing system 200 according to one embodiment of the present disclosure. Reconfigurable processing system 200 comprises N context-based operation reconfigurable instruction set processors (CRISPs), including exemplary CRISPs 100 a, 100 b, and 100 c, which are arbitrarily labeled CRISP 1, CRISP 2 and CRISP N. Reconfigurable processing system 200 further comprises real-time sequencer 210, sequence program memory 220, programmable interconnect fabric 230, and buffers 240 and 245. Buffers 240 and 245 are preferably bi-directional

Reconfiguration bits may be loaded into CRISPs 100 a, 100 b, and 100 c from the CONTROL line via real-time sequencer 210 and buffer 240. A control program may also be loaded into sequence program memory 220 from the CONTROL line via buffer 240. Real-time sequencer 210 sequences the contexts to be executed by each one of CRISPs 100 a-c by retrieving program instructions from program memory 220 and sending reconfiguration bits to CRISPs 100 a-c. In an exemplary embodiment, real-time sequencer 210 may comprise a stack processor, which is suitable to operate as a real-time scheduler due to its low latency and simplicity.

Reconfigurable interconnect fabric 230 provides connectivity between each one of CRISPs 100 a-c and an external DATA bus via bi-directional buffer 245. In one embodiment of the present disclosure, each one of CRISPs 100 a-c may act as a master of reconfigurable interconnect fabric 230 and may initiate address access. The bus arbiter for reconfigurable interconnect fabric 230 may be internal to real-time sequencer 210.

In an exemplary embodiment, reconfigurable processing system 200 may be, for example, a cell phone or a similar wireless device, or a data processor for use in a laptop computer. In a wireless device embodiment based on a software-defined radio (SDR) architecture, each one of CRISPs 100 a-c is responsible for executing a subset of context-related instructions that are associated with a particular reconfigurable function. For example, CRISP 100 a may be configured to execute context-related instructions that process CDMA baseband signals or OFDMA baseband signals. CRISP 100 b may be configured to execute context-related instructions that act as a memory controller. CRISP 100 c may be configured to execute context-related instructions that perform turbo decoding or Viterbi decoding.

Since CRISP devices are largely independent and may be run simultaneously, a Viterbi decoder implemented using one or more CRISP devices has the performance advantage of parallelism without incurring the full power penalty associated with running parallel operations. The loose coupling and independence of CRISP devices allows them to be configured for different systems and functions that may be shut down separately.

FIG. 3 is a high-level block diagram of multi-standard software-defined radio (SDR) system 300, which implements a reconfigurable Viterbi decoder using a CRISP device. SDR system 300 may comprise a wireless terminal (or mobile station, subscriber station, etc.) that accesses a wireless network, such as, for example, a GSM or CDMA cellular telephone, a PDA with WCDMA, IEEE-802.11x, HSDPA, OFDM/OFDMA capabilities, or the like.

Multi-standard SDR system 300 comprises baseband subsystem 301, applications subsystem 302, memory interface (IF) and peripherals subsystem 365, main control unit (MCU) 370, memory 375, and interconnect 380. MCU 370 may comprise, for example, a conventional microcontroller or a microprocessor (e.g., x86, ARM, RISC, DSP, etc.). Memory IF and peripherals subsystem 365 may connect SDR system 300 to an external memory (not shown) and to external peripherals (not shown). Memory 375 stores data from other components in SDR system 300 and from external devices (not shown). For example, memory 375 may store a stream of incoming data samples associated with a down-converted signal generated by radio frequency (RF) transceiver 385 and antenna 390 associated with SDR system 300. Interconnect 380 acts as a system bus that provides data transfer between subsystems 301 and 302, memory IF and peripherals subsystem 365, MCU 370, and memory 375.

Baseband subsystem 301 comprises real-time (RT) sequencer 305, memory 310, baseband DSP subsystem 315, interconnect 325, and a plurality of special purpose context-based operation instruction set processors (CRISPs), including transform CRISP 100 d, chip rate CRISP 100 e, symbol rate CRISP 100 f, and bit manipulation unit (BMU) CRISP 100 g. By way of example, transform CRISP 100 d may implement a Fast Fourier Transform (FFT) function, chip rate CRISP 100 e may implement a correlation function for a CDMA signal, and symbol rate CRISP 100 f may implement a turbo decoder function or a Viterbi decoder function.

In one such embodiment, transform CRISP 100 d may receive samples of an intermediate frequency (IF) signal stored in memory 375 and perform an FFT function that generates a sequence of chip samples at a baseband rate. Next, chip rate CRISP 100 e receives the chip samples from transform CRISP 100 d and performs a correlation function that generates a sequence of data symbols. Next, symbol rate CRISP 100 f receives the symbol data from chip rate CRISP 100 e and performs turbo decoding or Viterbi decoding to recover the baseband user data. The baseband user data may then be used by applications subsystem 302.

In an exemplary embodiment of the present disclosure, symbol rate CRISP 100 f may comprise two or more CRISPs that operate in parallel. Also, by way of example, BMU CRISP 100 g may implement such functions as variable length coding, cyclic redundancy check (CRC), convolutional encoding, and the like. Interconnect 325 acts as a system bus that provides data transfer between RT sequencer 305, memory 310, baseband DSP subsystem 315 and CRISPs 100 d-100 g.

Applications subsystem 302 comprises real-time (RT) sequencer 330, memory 335, multimedia DSP subsystem 340, interconnect 345, and multimedia macro-CRISP 350. Multimedia macro-CRISP 350 comprises a plurality of special purpose context-based operation instruction set processors, including MPEG-4/H.264 CRISP 100 h, transform CRISP 100 i, and BMU CRISP 100 j. In an exemplary embodiment of the disclosure, MPEG-4/H.264 CRISP 100 h performs motion estimation functions and transform CRISP 100 i performs a discrete cosine transform (DCT) function. Interconnect 380 provides data transfer between RT sequencer 330, memory 335, multimedia DSP subsystem 340, and multimedia macro-CRISP 350.

In the embodiment in FIG. 3, the use of CRISP devices enables applications subsystem 302 of multi-standard SDR system 300 to be reconfigured to support multiple video standards with multiple profiles and sizes. Additionally, the use of CRISP devices enables baseband subsystem 301 of multi-standard SDR system 300 to be reconfigured to support multiple air interface standards. Thus, SDR system 300 is able to operate in different types of wireless networks (e.g., CDMA, GSM, 802.11x, etc.) and can execute different types of video and audio formats. However, the use of CRISPS according to the principles of the present disclosure enables SDR system 300 to perform these functions with much lower power consumption than conventional wireless devices having comparable capabilities.

FIG. 4 is a block diagram of a reconfigurable Viterbi decoder implemented in CRISP 100 f according to the principles of the disclosure. The reconfigurable Viterbi decoder comprises branch metric calculation block 405, add-compare-select (ACS) and path metric calculation block 410, trellis and traceback calculation block 415, current stage memory buffer 420, next stage memory buffer 425, control logic and registers block 430, and address generation unit (AGU) 435. The reconfigurable Viterbi decoder is associated with memory 350, which may be a separate device from CRISP 100 f, as depicted in FIG. 4, or may be an integral part of CRISP 100 f. Memory 350 is logically divided into shared input symbol buffer 360 and shared trellis history buffer 370. In an exemplary embodiment, memory 350 may comprise a 32 Kbyte random access memory (RAM).

Control logic and registers block 430 comprises a programmable finite state machine and associated control registers that controls the overall operation of CRISP 100 f and reconfigures the functional blocks of the data path, including, for example, branch metric calculation block 405, add-compare-select (ACS) and path metric calculation block 410, trellis and traceback calculation block 415, current stage memory buffer 420, and next stage memory buffer 425.

Address generation unit (AGU) 435 controls the read and write operations to memory 350 that are made by branch metric calculation block 405 and trellis and traceback calculation block 415. Chip rate CRISP 100 e or another component in baseband subsystem 301 (shown in, for example, FIG. 3) writes symbol data samples into shared input symbol buffer 360. Branch metric calculation block 405 then reads the symbol data samples from shared input symbol buffer 360. Trellis and traceback calculation block 415 writes hard decoded trellis data in shared trellis history buffer 370 and reads back the stored hard decoded trellis data in later cycles.

As is well known, the Viterbi algorithm is often represented as a trellis diagram. The trellis is a time-indexed version of a state diagram. Each time instant in the trellis may contain, for example, two states, four states, eight states, or more. The maximum likelihood detection of a digital stream with intersymbol interference (ISI) can be described as finding the most probable path through a trellis of state transitions (branches). Each state corresponds to a possible pattern of recently received data bits and each branch of the trellis corresponds to the reception of the next (noisy) input. The branch metrics represent the costs of traversing along specific branches. The path (or state) metrics accumulate the minimum cost of arriving at a specific state. The path metrics are updated using an add-compare-select recursion. At each time instant, the branch metrics are added to the path (state) metrics of the previous time instant. The smallest path metric is then selected to be the new path metric for each state. Finally, after all of the input symbol samples are processed, the minimum path value represents the survivor sequence. Tracing backwards through the trellis gives the likely sequence of transmitted data.

Branch metric calculation block 405 is a reconfigurable device that is reconfigured by means of control parameters and data written into control logic and registers block 430. Branch metric calculation block 405 calculates four possible branch metric values by calculating the distances of input symbol samples from target values according to well-known principles. By way of example, branch metric calculation block 405 may read in 8-bit “soft” symbol samples (e.g., 256 level 2 s complement) from shared input symbol buffer 360 and may output four (4) branch metric values, each containing 16 bits. The branch metric values are typically Hamming distances (or other metric values) that are computed at each time instance for the paths between the states at the previous time instant and the states at the current time instant.

ACS and path metric calculation block 410 also is a reconfigurable device that is reconfigured by means of control parameters and data written into control logic and registers block 430. ACS and path metric calculation block 410 calculates the new value of the path (or state) metric at each stage (i.e., time instant) of the trellis. The minimum path metric is then output as a Stage Decision to trellis and traceback calculation block 415. ACS and path metric calculation block 410 reads the current path metrics (e.g., 4 path metrics) from current stage memory buffer 420 and adds the current path metrics to the branch metrics from branch metric calculation block 405 and stores the new path metrics in next stage memory buffer 425. Next stage memory buffer 425 and current stage memory buffer 420 then switch identities, so that next stage memory buffer 425 becomes current stage memory buffer 420 and current stage memory buffer 420 becomes next stage memory buffer 425.

Trellis and traceback calculation block 415 receives the stage decision values from ACS and path metric calculation block 410 and stores the hard decoded values in shared trails history buffer. When an entire block of symbol samples had been processed and the trellis is completed in shared trails history buffer, trellis and traceback calculation block 415 then traces back through the shortest path in the trellis in order to decode the input sequence.

In an advantageous embodiment, the reconfigurable Viterbi decoder in CRISP 100 f provides fully programmable feed-forward channel decoding and traceback sessions that support: i) channel decoding with constraint lengths of, for example, K=5, K=6, K=7 and K=9; ii) puncture codes; and iii) rate=½, ⅓, ¼ and ⅙ by four, fully programmable polynomials. Shared trellis history buffer 370 may support up to 512 stages for K=9 or 8192 stages for K=5. The reconfigurable Viterbi decoder in CRISP 100 f further supports a block length of symbol data in memory 350 that is fully programmable and also supports programmable traceback methods.

An external controller, such as baseband DSP subsystem 315 or MCU 370 programs the reconfigurable Viterbi decoder while CRISP 100 f is in Wake-Up mode. The reconfigurable Viterbi decoder in CRISP 100 f can then execute independently on the whole block of data in memory 350 based on the programmed registers and the dedicated instructions in control logic and registers block 430. Data is written to and read from memory 350 via, for example, the bus in interconnect 325. The addresses of shared input symbol buffer 360 and shared trellis history buffer 370 are fully programmable.

FIGS. 5-8 illustrate the pipelined architectures of selected components in CRISPS 100 f and of the convolutional coder in the transmitting device. FIG. 5 is timing diagram of selected pipeline stages in a convolution encoder that encodes the data that the Viterbi decoder decodes. The convolutional encoder comprises an encoder load stage, an encoder shift stage, and an encoder store stage. Sequential time periods T0, T1, and T2 are shown. The convolutional encoder receives five sequential blocks of data in the following order (from left to right): [ . . . , N−2, N−1, N, N+1, N+2]. During T0, the encoder load stage processes the Data N block, the encoder shift stage processes the Data N−1 block, and the encoder store stage processes the Data N−2 block. During T1, the encoder load stage processes the Data N+1 block, the encoder shift stage processes the Data N block, and the encoder store stage processes the Data N−1 block. During T3, the encoder load stage processes the Data N+2 block, the encoder shift stage processes the Data N+1 block, and the encoder store stage processes the Data N block.

FIG. 6 is timing diagram of selected pipeline stages in a branch metric calculation block 405. Branch metric calculation block 405 comprises a branch metric load stage, a branch metric calculation stage, and a branch metric store stage. Branch metric calculation block 405 receives five sequential blocks of data in the following order (from left to right): [ . . . , N−2, N−1, N, N+1, N+2]. During T0, the branch metric load stage processes the Data N block, the branch metric calculation stage processes the Data N−1 block, and the branch metric store stage processes the Data N−2 block. During T1, the branch metric load stage processes the Data N+1 block, the branch metric calculation stage processes the Data N block, and the branch metric store stage processes the Data N−1 block. During T3, the branch metric load stage processes the Data N+2 block, the branch metric calculation stage processes the Data N+1 block, and the encoder store stage processes the Data N block.

FIG. 7 is timing diagram of selected pipeline stages in a path metric calculation block. The path metric calculation block comprises a path metric pre-load stage, a path metric load stage, a path metric calculation ACS stage, and a path metric store stage. The path metric calculation block receives six sequential blocks of data in the following order (from left to right): [ . . . , N−2, N−1, N, N+1, N+2, N+3]. During T0, the path metric pre-load stage processes the Data N+1 block, the path metric load stage processes the Data N block, the path metric calculation ACS stage processes the Data N−1 block, and the path metric store stage processes the Data N−2 block. During T1, the path metric pre-load stage processes the Data N+2 block, the path metric load stage processes the Data N+1 block, the path metric calculation ACS stage processes the Data N block, and the path metric store stage processes the Data N−1 block. During T2, the path metric pre-load stage processes the Data N+3 block, the path metric load stage processes the Data N+2 block, the path metric calculation ACS stage processes the Data N+1 block, and the path metric store stage processes the Data N block.

FIG. 8 is timing diagram of selected pipeline stages in trellis and traceback calculation block 415. Trellis and traceback calculation block 415 comprises a trellis store stage, a traceback load stage, and a traceback shift stage. Trellis and traceback calculation block 415 receives five sequential blocks of data in the following order (from left to right): [ . . . , N−2, N−1, N, N+1, N+2]. During T0, the trellis store stage processes the Data N block, the traceback load stage processes the Data N−1 block, and the traceback shift stage processes the Data N−2 block. During T1, the trellis store stage processes the Data N+1 block, the traceback load stage processes the Data N block, and the traceback shift stage processes the Data N−1 block. During T2, the trellis store stage processes the Data N+2 block, the traceback load stage processes the Data N+1 block, and the traceback shift stage processes the Data N block.

FIG. 9 illustrates one common scenario 900 where an occurrence of ISI caused by, for example, channel delays between a base station (BS) 902 and a mobile station (MS) 904 moving away from BS 902. In such a scenario, ISI between BS 902 and MS 904 does not necessarily occur directly. A receiver in the MS 904, for example, receives a distorted signal because of the path delays and/or different communication paths that the signal must travel through.

ISI is likely to occur when two input pulses originate from two different sources and/or when two input symbols experience two different delays. Similar interference issues occur with various wireless communication systems, including for example, Gaussian minimum shift keying (GMSK) and global systems for mobile (GSM) communication systems. To prevent the various causes of ISI, the present disclosure provides, for example, a system and method for supporting channel equalization for Viterbi CRISP. In particular, one embodiment of the present disclosure provides channel Maximum Likelihood Sequence Estimation (MLSE) equalization for multiple standards using the Viterbi algorithm for use in systems, such as, SDR systems.

FIG. 10A is a somewhat simplified block diagram of a MLSE channel equalizer 1000 according to one embodiment of the present disclosure. MLSE channel equalizer 1000 includes matched filter 1002 and Viterbi algorithm block 1004. Matched filter 1102 generally receives symbols from a transmitter and performs channel estimation using a filter, such as an FIR filter. Matched filter 1002 maximizes the signal to noise ratio (SNR) and aligns the signal to a common clock signal. Viterbi algorithm block 1004 removes any past dependencies and noise before decoding and outputs equalized symbols.

FIG. 10B is a more detailed block diagram of MLSE channel equalizer 1000 shown in FIG. 10A. Here, MLSE channel equalizer 1000 includes matched filter 1002, noise whitening (NW) filter 1006, MLSE Viterbi Algorithm block 1008 and channel estimation circuit 1010. Matched filter 1002 generally receives a signal, r(t), from a transmitter and passes it through matched filter 1002. The output of matched filter 1002 is passed through NW filter 1006. The output of NW filter 1006, y(k), is directed to channel estimation circuit 1010 and MLSE Viterbi Algorithm block 1008, where y is a sample sequence.

The output of MLSE Viterbi Algorithm block 1008, {circumflex over (b)}(k), is passed to channel estimation circuit 1010, where {circumflex over (b)} is a symbol sequence estimate. The output of channel estimation circuit 1010, {circumflex over (f)}(k), is in turn passed on to matched filter 1002 and MLSE Viterbi Algorithm block 1008, where {circumflex over (f)} is the probability of receiving sample sequence y of length N, conditioned on a certain symbol sequence estimate {circumflex over (b)} and an overall channel estimate {circumflex over (f)}.

In one embodiment, taking into account additive white Gaussian noise (AWGN), MLSE channel equalizer 1000 finds a symbol sequence that maximizes {circumflex over (f)} as shown by a relationship shown in Equation 1 below.

$\begin{matrix} {{p\left( {\left. y \middle| \hat{b} \right.,\hat{f}} \right)} = {\prod\limits_{k = 1}^{N}\;{p\left( {\left. y_{k} \middle| \hat{b} \right.,\hat{f}} \right)}}} & \left\lbrack {{Eqn}.\mspace{14mu} 1} \right\rbrack \\ {{p\left( {\left. y \middle| \hat{b} \right.,\hat{f}} \right)} = {\frac{1}{\left( {2\pi} \right)^{N/2}\sigma^{\;^{N}}}\exp\left\{ {{- \frac{1}{2\sigma^{2}}}{\sum\limits_{k = 1}^{N}{{y_{k} - {\sum\limits_{n = 0}^{K - 1}{{\hat{f}}_{n}{\hat{b}}_{k - n}}}}}^{2}}} \right\}}} & \left\lbrack {{Eqn}.\mspace{14mu} 2} \right\rbrack \end{matrix}$

In Equation 2, the term

$\sum\limits_{n = 0}^{K - 1}{{\hat{f}}_{n}{\hat{b}}_{k - n}}$

is the length of f(k) In other words, MLSE channel equalizer 1000 finds a symbol sequence estimate and overall channel estimate that maximizes the conditional probability represented by {circumflex over (f)}.

Accordingly, if the length of the overall channel impulse response in samples (or channel coefficients) is K, then the time span of the channel may be represented by (K−1) T. A state trellis is constructed where a state is defined as a certain combination of K−1 previous symbols causing ISI on the kth symbol. At adjacent time instants, the symbol sequences causing ISI are generally correlated and the “best” state sequence is estimated by the Viterbi algorithm. Of the transitions terminating in a certain state at a certain time instant, the Viterbi algorithm generally selects the transition associated with the highest accumulated probability (up to that time instant) for further processing.

FIG. 11 is a somewhat simplified block diagram of a Viterbi CRISP 1100 according to one embodiment of the present disclosure. Viterbi CRISP 1100 includes branch metric calculation block 1102, ACS block 1104 and instruction decoder and dispatcher block 1106. Viterbi CRISP 1100 preferably allows several inputs and processes the inputs and information from branch metric calculation block 1102 and ACS block 1104 according to instruction decoder and dispatcher block 1106.

Instruction decoder and dispatcher block 1106 is preferably controlled by CRISP devices. Branch metric calculation block 1102 includes inputs such as, for example, the output of matched filter 1002, channel parameters (from, for example, channel estimation circuit 1010), and a sign select that assigns a polarity to the inputs.

The output of branch metric calculation block 1102 is passed to ACS 1104. Other inputs to ACS 1104 include a trellis select describing where the current state originated from and a path metric RD-bus signal that supplies a probability for each of the eight states. ACS 1104 finally outputs a soft/hard decision to a trellis memory (not shown) and a path metric WR_bus that reports current states to the trellis memory.

It should be understood, however, that although the present disclosure describes techniques incorporating MLSE channel equalizer 1000, other optimizing techniques using other suitable equalizers such as, for example, Reduce State Sequence Estimation (RSSE) and Delayed Decision Feedback Sequence Estimation (DDFSE) may also be used in conjunction with any suitable constellation to support multiple standards according to one embodiment of the present disclosure.

FIG. 12 is a somewhat simplified block diagram of method or technique 1200 for channel equalization using Viterbi algorithms. In step 1205, a receiver in a communication system receives an input signal from an associated transmitter.

In step 1210, the input signal is passed through a matched filter, such as matched filter 1002. Matched filter 1002 generally parses the symbols from the received input signal and performs channel estimation. In step 1215, the channel information is correlated in a channel estimation circuit such as, for example, channel estimation circuit 1010.

The result of steps 1210 and 1215, are passed on to branch calculation block in a Viterbi algorithm block such as, for example, branch metric calculation block 1102 in Viterbi CRISP 1100 in step 1220. In step 1225, branch metric calculation block 1102 processes the information and correlates an output to an add-and-compare block such as, for example, ACS block 1104. Finally, in step 1230, Viterbi CRISP 1100 outputs the equalized symbols associated with the original input signal.

Although the present disclosure has been described with an exemplary embodiment, various changes and modifications may be suggested to one skilled in the art. It is intended that the present disclosure encompass such changes and modifications as fall within the scope of the appended claims. 

1. An apparatus for channel equalization using a Viterbi algorithm, the apparatus comprising: a reconfigurable data path including: i) a reconfigurable branch metric calculation block correlating information from an output of a matched filter and channel parameters from a channel estimation circuit; and ii) a reconfigurable add-compare-select and path metric calculation block; and a programmable finite state machine capable of controlling the reconfigurable data path, wherein the programmable finite state machine executes a plurality of context-related instructions associated with the Viterbi algorithm.
 2. The apparatus as set forth in claim 1, wherein the channel estimation circuit is a channel equalizer.
 3. The apparatus as set forth in claim 2, wherein the channel equalizer is at least one of: a maximum likelihood sequence estimation equalizer (MLSE), a Reduce State Sequence Estimation (RSSE) equalizer, and a Delayed Decision Feedback Sequence Estimation (DDFSE) equalizer.
 4. The apparatus as set forth in claim 1, wherein the reconfigurable data path is configured by reconfiguration bits received from an external device.
 5. The apparatus as set forth in claim 4, wherein the programmable finite state machine is configured by reconfiguration bits received from the external device.
 6. The apparatus as set forth in claim 5, wherein the apparatus may be put into a low power mode by the external controller during a time period in which the Viterbi decoder is not required to decode input data.
 7. The apparatus as set forth in claim 1, wherein the reconfigurable branch metric calculation block comprises a plurality of pipelined stages capable of calculating branch metric values from input symbol samples.
 8. The apparatus as set forth in claim 7, wherein the reconfigurable add-compare-select and path metric calculation block comprises a plurality of pipelined stages capable of calculating path metric values from branch metric values received from the reconfigurable branch metric calculation block.
 9. A software-defined radio (SDR) system comprising: a reconfigurable radio-frequency (RF) transceiver portion to operate under a plurality of wireless communication standards; and a reconfigurable baseband processing portion associated with the RF transceiver portion, the reconfigurable baseband processing portion including a Viterbi algorithm comprising: a reconfigurable data path including: i) a reconfigurable branch metric calculation block correlating information from an output of a matched filter and channel parameters from a channel estimation circuit; and ii) a reconfigurable add-compare-select and path metric calculation block; and a programmable finite state machine to control the reconfigurable data path, wherein the programmable finite state machine executes a plurality of context-related instructions associated with the Viterbi algorithm.
 10. The SDR system as set forth in claim 9, wherein the channel estimation circuit is a channel equalizer.
 11. The SDR system as set forth in claim 10, wherein the channel equalizer is at least one of: a maximum likelihood sequence estimation equalizer (MLSE), a Reduce State Sequence Estimation (RSSE) equalizer, and a Delayed Decision Feedback Sequence Estimation (DDFSE) equalizer.
 12. The SDR system as set forth in claim 9, wherein the reconfigurable data path is configured by reconfiguration bits received from an external device.
 13. The SDR system as set forth in claim 12, wherein the programmable finite state machine is configured by reconfiguration bits received from the external device.
 14. The SDR system as set forth in claim 9, the system may be put into a low power mode by the external controller during a time period in which the reconfigurable baseband processing portion is not required to decode input data.
 15. The SDR system as set forth in claim 9, wherein the reconfigurable branch metric calculation block comprises a plurality of pipelined stages capable of calculating branch metric values from input symbol samples.
 16. A method of channel equalization using a Viterbi algorithm comprising: correlating information from an output of a matched filter and channel parameters from a channel estimation circuit; passing the correlated information to a reconfigurable data path comprising: i) a reconfigurable branch metric calculation block; and ii) a reconfigurable add-compare-select and path metric calculation block; and controlling the reconfigurable data path with a programmable finite state machine, wherein the programmable finite state machine executes a plurality of context-related instructions associated with the Viterbi algorithm.
 17. The method as set forth in claim 16, wherein the channel estimation circuit is a channel equalizer.
 18. The method as set forth in claim 17, wherein the channel equalizer is at least one of: a maximum likelihood sequence estimation equalizer (MLSE), a Reduce State Sequence Estimation (RSSE) equalizer, and a Delayed Decision Feedback Sequence Estimation (DDFSE) equalizer.
 19. The method as set forth in claim 16 further comprising: configuring the reconfigurable data path using reconfiguration bits received from an external device.
 20. The method as set forth in claim 19 further comprising: configuring the programmable finite state machine using reconfiguration bits received from the external device. 